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Application of deep-learning to the seronegative side of the NMO spectrum

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Abstract

Objectives

To apply a deep-learning algorithm to brain MRIs of seronegative patients with neuromyelitis optica spectrum disorders (NMOSD) and NMOSD-like manifestations and assess whether their structural features are similar to aquaporin-4-seropositive NMOSD or multiple sclerosis (MS) patients.

Patients and methods

We analyzed 228 T2- and T1-weighted brain MRIs acquired from aquaporin-4-seropositive NMOSD (n = 85), MS (n = 95), aquaporin-4-seronegative NMOSD [n = 11, three with anti-myelin oligodendrocyte glycoprotein antibodies (MOG)], and aquaporin-4-seronegative patients with NMOSD-like manifestations (idiopathic recurrent optic neuritis and myelitis, n = 37), who were recruited from February 2010 to December 2019. Seventy-three percent of aquaporin-4-seronegative patients with NMOSD-like manifestations also had a clinical follow-up (median duration of 4 years). The deep-learning neural network architecture was based on four 3D convolutional layers. It was trained and validated on MRI scans of aquaporin-4-seropositive NMOSD and MS patients and was then applied to aquaporin-4-seronegative NMOSD and NMOSD-like manifestations. Assignment of unclassified aquaporin-4-seronegative patients was compared with their clinical follow-up.

Results

The final algorithm differentiated aquaporin-4-seropositive NMOSD and MS patients with an accuracy of 0.95. All aquaporin-4-seronegative NMOSD and 36/37 aquaporin-4-seronegative patients with NMOSD-like manifestations were classified as NMOSD. Anti-MOG patients had a similar probability of being NMOSD or MS. At clinical follow-up, one unclassified aquaporin-4-seronegative patient evolved to MS, three developed NMOSD, and the others did not change phenotype.

Conclusions

Our findings support the inclusion of aquaporin4-seronegative patients into NMOSD and suggest a possible expansion to aquaporin-4-seronegative unclassified patients with NMOSD-like manifestations. Anti-MOG patients are likely to have intermediate brain features between NMOSD and MS.

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Data availability

The dataset analyzed and the final algorithm are available on reasonable request.

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Funding

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Authors and Affiliations

  1. Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy

    Laura Cacciaguerra, Loredana Storelli, Massimo Filippi & Maria A. Rocca

  2. Vita-Salute San Raffaele University, Milan, Italy

    Laura Cacciaguerra, Massimo Filippi & Maria A. Rocca

  3. Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy

    Marta Radaelli, Lucia Moiola, Massimo Filippi & Maria A. Rocca

  4. Clinic of Neurology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia

    Sarlota Mesaros & Jelena Drulovic

  5. Neurorehabilitation Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy

    Massimo Filippi

  6. Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy

    Massimo Filippi

Authors
  1. Laura Cacciaguerra
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  2. Loredana Storelli
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  5. Lucia Moiola
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  7. Massimo Filippi
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  8. Maria A. Rocca
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Contributions

LC: data analysis, statistical analysis, drafting/revising the manuscript. LS: data analysis, statistical analysis, drafting/revising the manuscript. MR: patient recruitment, clinical assessment, data analysis. SM: patient recruitment, clinical assessment, data analysis. LM: patient recruitment, clinical assessment, data analysis. JD: patient recruitment, clinical assessment, data analysis. MF: study concept, drafting/revising the manuscript. MAR: study concept, drafting/revising the manuscript, MRI data analysis.

Corresponding author

Correspondence to Maria A. Rocca.

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The authors have no conflicts of interest to declare that are relevant to the content of this article.

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Approval was received from the local ethical standards committee (IRCCS San Raffaele Scientific Institute) on human experimentation. The study conforms to the Declaration of Helsinki.

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All subjects signed a written informed consent before study participation.

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Cacciaguerra, L., Storelli, L., Radaelli, M. et al. Application of deep-learning to the seronegative side of the NMO spectrum. J Neurol 269, 1546–1556 (2022). https://doi.org/10.1007/s00415-021-10727-y

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  • DOI: https://doi.org/10.1007/s00415-021-10727-y

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